Camera calibration has been done on the basis of point-based targets like checkerboards, but such targets are not practical for outdoor-based cameras, such as those used in outdoor sporting environments. Standard camera calibration algorithms employ a two-stage approach that first identifies point correspondences between geometric features in an image and in the world (e.g., a checkerboard's junction points from its camera's image projection and from the real world positions), and then finds the perspective transformation parameters which minimize the distance between the transformed world points and their corresponding image locations. Checkerboards are perhaps the most popular target because of their ease of use and manufacture, but in large outdoor scenes, checkerboards are impractical, as the necessary size may be on the order of meters. Furthermore, detecting a sufficient number of landmark point features continuously from all camera field-of-views may be challenging given the highly dynamic steering of a broadcast camera (especially when covering a sporting event where camera panning and zooming may be rapid and extreme). What is needed is a camera calibration technique that relies on non-point landmarks in a scene, also known as an image-based alignment. Contrary to a feature-based alignment method, for example, an image-based alignment method aligns non-point landmarks in a reference image with counterpart non-point landmarks in the scene image by warping one image onto the other and estimating the warping function parameters that minimizes the difference between the reference image and the scene image. In general, image-based alignment does not require the step of extracting feature points in the reference and scene images and finding corresponding pairs (as in the feature-based alignment) and allows for accurate matching of the two images. On the other hand image-based alignment requires the two images to be already coarsely aligned via an initialization process, often based on feature-based alignment.
Calibration of a broadcast camera covering a dynamic event such as a football game, may be complicated by the fact that field surface is not planar. A method that takes into account a non-planar surface is needed to calibrate the camera automatically and dynamically as the camera pans and zooms rapidly during the capturing of a live event. Furthermore, an efficient method is needed for the calibration of a spatially coupled set of cameras (e.g., a stereoscopic camera) capturing a live event at a scene including a non-planar surface.